The consumer video industry has been undergoing significant changes over the past few years due to the transition from analog to digital transmission and storage and the adoption of new video formats, including high definition. In parallel, digital display technologies are rapidly converting from the old CRT to new digital technologies, including LCD, plasma, and DLP.
Consumer expectations for video image quality are rising and digital high definition displays are capable of resolving increasingly fine details. For this reason, consumers are becoming less tolerant of noise and distortion in video images.
The term “noise” is used informally as a catch-all term that when applied to video images means almost anything that looks unnatural or diminishes the clarity of the video image. The term has traditionally been used to describe stray or random electrical signals that are imposed on a video signal. A characteristic of this type of noise is that the unwanted noise signal is uncorrelated to the video signal. Traditional techniques for removing this type of uncorrelated noise include temporal averaging, in which multiple video frames are averaged to diminish the appearance of noise. This type of temporal averaging requires motion detection, because the temporal averaging must be applied only to areas of the video where there is no motion to prevent motion blurring. This also limits the noise suppression to areas in the video image where there is no motion.
Increasingly, video signals are transmitted digitally. Digital transmission requires the use of digital compression to reduce transmission bandwidth and storage requirements. In the United States, the FCC has mandated the MPEG2 video compression standard for digital terrestrial broadcast. Cable and satellite providers may use MPEG2 or they may use other standards, such as H.264. Most video compression standards are “lossy.” This means that the compressed video is not identical the pre-compressed video. As the compression ratio is increased, the lossy compression standards result in increasing distortion in the compressed video.
The distortion introduced by video compression is also informally referred to as “noise.” But it is actually an artifact of the video compression processing. It is unlike the random noise described earlier in that it is correlated to image details or to rapid motion. Since it is correlated to the image content of the video, the temporal averaging technique described above is not effective in removing noise due to compression processing because this type of noise is correlated to motion of feature elements of the video image.
Compression processing introduces a number of distortions in the compressed video signal. The most common types of distortion are referred to as “block noise” and “mosquito noise.” Both of these types of noise are objectionable to a viewer because they are clearly unnatural in appearance.
Block noise typically appears in compressed video in areas of rapid motion. If the video compression and transmission system can not provide enough new information to update rapid motion, then an entire DCT block of pixels may be temporarily assigned a single color. The term “DCT” means “Discreet Cosine Transform” which is a mathematical operation used in most compression standards. The size of a DCT block of pixels in the MPEG2 compression standard is 8×8 pixels, so in areas of rapid motion, an 8×8 block of pixels can be assigned a single color. This results in the temporary appearance of blocks of 8×8 in the video image.
Mosquito noise is another artifact of lossy compression processing. Mosquito noise appears in the video image as small dots or distortions in the luma value of pixels that are near the edges of objects. Object edges convert to high frequencies by the DCT process. Mosquito noise is the result of course quantization of the higher frequency components of a video image as a result of compression processing. Mosquito noise will appear in close proximity to object edges. Mosquito noise will be distributed within the same 8×8 block as the pixels that make up the object edge. This bounds the area where mosquito noise is visible.
In addition to the block noise and mosquito noise artifacts described previously, compression processing tends to remove detail from video images.
What is needed is a method that can remove compression artifacts and enhance video images.
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